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|
- # -*- coding: utf-8 -*-
- '''
- @Time : 2020/05/06 15:07
- @Author : Tianxiaomo
- @File : train.py
- @Noice :
- @Modificattion :
- @Author :
- @Time :
- @Detail :
- '''
- import time
- import logging
- import os, sys, math
- import argparse
- from collections import deque
- import datetime
- import cv2
- from tqdm import tqdm
- import numpy as np
- import torch
- import torch.nn as nn
- from torch.utils.data import DataLoader
- from torch import optim
- from torch.nn import functional as F
- from tensorboardX import SummaryWriter
- from easydict import EasyDict as edict
- from dataset import Yolo_dataset
- from cfg import Cfg
- from models import Yolov4
- from tool.darknet2pytorch import Darknet
- from tool.tv_reference.utils import collate_fn as val_collate
- from tool.tv_reference.coco_utils import convert_to_coco_api
- from tool.tv_reference.coco_eval import CocoEvaluator
- def bboxes_iou(bboxes_a, bboxes_b, xyxy=True, GIoU=False, DIoU=False, CIoU=False):
- """Calculate the Intersection of Unions (IoUs) between bounding boxes.
- IoU is calculated as a ratio of area of the intersection
- and area of the union.
- Args:
- bbox_a (array): An array whose shape is :math:`(N, 4)`.
- :math:`N` is the number of bounding boxes.
- The dtype should be :obj:`numpy.float32`.
- bbox_b (array): An array similar to :obj:`bbox_a`,
- whose shape is :math:`(K, 4)`.
- The dtype should be :obj:`numpy.float32`.
- Returns:
- array:
- An array whose shape is :math:`(N, K)`. \
- An element at index :math:`(n, k)` contains IoUs between \
- :math:`n` th bounding box in :obj:`bbox_a` and :math:`k` th bounding \
- box in :obj:`bbox_b`.
- from: https://github.com/chainer/chainercv
- https://github.com/ultralytics/yolov3/blob/eca5b9c1d36e4f73bf2f94e141d864f1c2739e23/utils/utils.py#L262-L282
- """
- if bboxes_a.shape[1] != 4 or bboxes_b.shape[1] != 4:
- raise IndexError
- if xyxy:
- # intersection top left
- tl = torch.max(bboxes_a[:, None, :2], bboxes_b[:, :2])
- # intersection bottom right
- br = torch.min(bboxes_a[:, None, 2:], bboxes_b[:, 2:])
- # convex (smallest enclosing box) top left and bottom right
- con_tl = torch.min(bboxes_a[:, None, :2], bboxes_b[:, :2])
- con_br = torch.max(bboxes_a[:, None, 2:], bboxes_b[:, 2:])
- # centerpoint distance squared
- rho2 = ((bboxes_a[:, None, 0] + bboxes_a[:, None, 2]) - (bboxes_b[:, 0] + bboxes_b[:, 2])) ** 2 / 4 + (
- (bboxes_a[:, None, 1] + bboxes_a[:, None, 3]) - (bboxes_b[:, 1] + bboxes_b[:, 3])) ** 2 / 4
- w1 = bboxes_a[:, 2] - bboxes_a[:, 0]
- h1 = bboxes_a[:, 3] - bboxes_a[:, 1]
- w2 = bboxes_b[:, 2] - bboxes_b[:, 0]
- h2 = bboxes_b[:, 3] - bboxes_b[:, 1]
- area_a = torch.prod(bboxes_a[:, 2:] - bboxes_a[:, :2], 1)
- area_b = torch.prod(bboxes_b[:, 2:] - bboxes_b[:, :2], 1)
- else:
- # intersection top left
- tl = torch.max((bboxes_a[:, None, :2] - bboxes_a[:, None, 2:] / 2),
- (bboxes_b[:, :2] - bboxes_b[:, 2:] / 2))
- # intersection bottom right
- br = torch.min((bboxes_a[:, None, :2] + bboxes_a[:, None, 2:] / 2),
- (bboxes_b[:, :2] + bboxes_b[:, 2:] / 2))
- # convex (smallest enclosing box) top left and bottom right
- con_tl = torch.min((bboxes_a[:, None, :2] - bboxes_a[:, None, 2:] / 2),
- (bboxes_b[:, :2] - bboxes_b[:, 2:] / 2))
- con_br = torch.max((bboxes_a[:, None, :2] + bboxes_a[:, None, 2:] / 2),
- (bboxes_b[:, :2] + bboxes_b[:, 2:] / 2))
- # centerpoint distance squared
- rho2 = ((bboxes_a[:, None, :2] - bboxes_b[:, :2]) ** 2 / 4).sum(dim=-1)
- w1 = bboxes_a[:, 2]
- h1 = bboxes_a[:, 3]
- w2 = bboxes_b[:, 2]
- h2 = bboxes_b[:, 3]
- area_a = torch.prod(bboxes_a[:, 2:], 1)
- area_b = torch.prod(bboxes_b[:, 2:], 1)
- en = (tl < br).type(tl.type()).prod(dim=2)
- area_i = torch.prod(br - tl, 2) * en # * ((tl < br).all())
- area_u = area_a[:, None] + area_b - area_i
- iou = area_i / area_u
- if GIoU or DIoU or CIoU:
- if GIoU: # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
- area_c = torch.prod(con_br - con_tl, 2) # convex area
- return iou - (area_c - area_u) / area_c # GIoU
- if DIoU or CIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
- # convex diagonal squared
- c2 = torch.pow(con_br - con_tl, 2).sum(dim=2) + 1e-16
- if DIoU:
- return iou - rho2 / c2 # DIoU
- elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
- v = (4 / math.pi ** 2) * torch.pow(torch.atan(w1 / h1).unsqueeze(1) - torch.atan(w2 / h2), 2)
- with torch.no_grad():
- alpha = v / (1 - iou + v)
- return iou - (rho2 / c2 + v * alpha) # CIoU
- return iou
- class Yolo_loss(nn.Module):
- def __init__(self, n_classes=80, n_anchors=3, device=None, batch=2):
- super(Yolo_loss, self).__init__()
- self.device = device
- self.strides = [8, 16, 32]
- image_size = 608
- self.n_classes = n_classes
- self.n_anchors = n_anchors
- self.anchors = [[12, 16], [19, 36], [40, 28], [36, 75], [76, 55], [72, 146], [142, 110], [192, 243], [459, 401]]
- self.anch_masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
- self.ignore_thre = 0.5
- self.masked_anchors, self.ref_anchors, self.grid_x, self.grid_y, self.anchor_w, self.anchor_h = [], [], [], [], [], []
- for i in range(3):
- all_anchors_grid = [(w / self.strides[i], h / self.strides[i]) for w, h in self.anchors]
- masked_anchors = np.array([all_anchors_grid[j] for j in self.anch_masks[i]], dtype=np.float32)
- ref_anchors = np.zeros((len(all_anchors_grid), 4), dtype=np.float32)
- ref_anchors[:, 2:] = np.array(all_anchors_grid, dtype=np.float32)
- ref_anchors = torch.from_numpy(ref_anchors)
- # calculate pred - xywh obj cls
- fsize = image_size // self.strides[i]
- grid_x = torch.arange(fsize, dtype=torch.float).repeat(batch, 3, fsize, 1).to(device)
- grid_y = torch.arange(fsize, dtype=torch.float).repeat(batch, 3, fsize, 1).permute(0, 1, 3, 2).to(device)
- anchor_w = torch.from_numpy(masked_anchors[:, 0]).repeat(batch, fsize, fsize, 1).permute(0, 3, 1, 2).to(
- device)
- anchor_h = torch.from_numpy(masked_anchors[:, 1]).repeat(batch, fsize, fsize, 1).permute(0, 3, 1, 2).to(
- device)
- self.masked_anchors.append(masked_anchors)
- self.ref_anchors.append(ref_anchors)
- self.grid_x.append(grid_x)
- self.grid_y.append(grid_y)
- self.anchor_w.append(anchor_w)
- self.anchor_h.append(anchor_h)
- def build_target(self, pred, labels, batchsize, fsize, n_ch, output_id):
- # target assignment
- tgt_mask = torch.zeros(batchsize, self.n_anchors, fsize, fsize, 4 + self.n_classes).to(device=self.device)
- obj_mask = torch.ones(batchsize, self.n_anchors, fsize, fsize).to(device=self.device)
- tgt_scale = torch.zeros(batchsize, self.n_anchors, fsize, fsize, 2).to(self.device)
- target = torch.zeros(batchsize, self.n_anchors, fsize, fsize, n_ch).to(self.device)
- # labels = labels.cpu().data
- nlabel = (labels.sum(dim=2) > 0).sum(dim=1) # number of objects
- truth_x_all = (labels[:, :, 2] + labels[:, :, 0]) / (self.strides[output_id] * 2)
- truth_y_all = (labels[:, :, 3] + labels[:, :, 1]) / (self.strides[output_id] * 2)
- truth_w_all = (labels[:, :, 2] - labels[:, :, 0]) / self.strides[output_id]
- truth_h_all = (labels[:, :, 3] - labels[:, :, 1]) / self.strides[output_id]
- truth_i_all = truth_x_all.to(torch.int16).cpu().numpy()
- truth_j_all = truth_y_all.to(torch.int16).cpu().numpy()
- for b in range(batchsize):
- n = int(nlabel[b])
- if n == 0:
- continue
- truth_box = torch.zeros(n, 4).to(self.device)
- truth_box[:n, 2] = truth_w_all[b, :n]
- truth_box[:n, 3] = truth_h_all[b, :n]
- truth_i = truth_i_all[b, :n]
- truth_j = truth_j_all[b, :n]
- # calculate iou between truth and reference anchors
- anchor_ious_all = bboxes_iou(truth_box.cpu(), self.ref_anchors[output_id], CIoU=True)
- # temp = bbox_iou(truth_box.cpu(), self.ref_anchors[output_id])
- best_n_all = anchor_ious_all.argmax(dim=1)
- best_n = best_n_all % 3
- best_n_mask = ((best_n_all == self.anch_masks[output_id][0]) |
- (best_n_all == self.anch_masks[output_id][1]) |
- (best_n_all == self.anch_masks[output_id][2]))
- if sum(best_n_mask) == 0:
- continue
- truth_box[:n, 0] = truth_x_all[b, :n]
- truth_box[:n, 1] = truth_y_all[b, :n]
- pred_ious = bboxes_iou(pred[b].reshape(-1, 4), truth_box, xyxy=False)
- pred_best_iou, _ = pred_ious.max(dim=1)
- pred_best_iou = (pred_best_iou > self.ignore_thre)
- pred_best_iou = pred_best_iou.view(pred[b].shape[:3])
- # set mask to zero (ignore) if pred matches truth
- obj_mask[b] = ~ pred_best_iou
- for ti in range(best_n.shape[0]):
- if best_n_mask[ti] == 1:
- i, j = truth_i[ti], truth_j[ti]
- a = best_n[ti]
- obj_mask[b, a, j, i] = 1
- tgt_mask[b, a, j, i, :] = 1
- target[b, a, j, i, 0] = truth_x_all[b, ti] - truth_x_all[b, ti].to(torch.int16).to(torch.float)
- target[b, a, j, i, 1] = truth_y_all[b, ti] - truth_y_all[b, ti].to(torch.int16).to(torch.float)
- target[b, a, j, i, 2] = torch.log(
- truth_w_all[b, ti] / torch.Tensor(self.masked_anchors[output_id])[best_n[ti], 0] + 1e-16)
- target[b, a, j, i, 3] = torch.log(
- truth_h_all[b, ti] / torch.Tensor(self.masked_anchors[output_id])[best_n[ti], 1] + 1e-16)
- target[b, a, j, i, 4] = 1
- target[b, a, j, i, 5 + labels[b, ti, 4].to(torch.int16).cpu().numpy()] = 1
- tgt_scale[b, a, j, i, :] = torch.sqrt(2 - truth_w_all[b, ti] * truth_h_all[b, ti] / fsize / fsize)
- return obj_mask, tgt_mask, tgt_scale, target
- def forward(self, xin, labels=None):
- loss, loss_xy, loss_wh, loss_obj, loss_cls, loss_l2 = 0, 0, 0, 0, 0, 0
- for output_id, output in enumerate(xin):
- batchsize = output.shape[0]
- fsize = output.shape[2]
- n_ch = 5 + self.n_classes
- output = output.view(batchsize, self.n_anchors, n_ch, fsize, fsize)
- output = output.permute(0, 1, 3, 4, 2) # .contiguous()
- # logistic activation for xy, obj, cls
- output[..., np.r_[:2, 4:n_ch]] = torch.sigmoid(output[..., np.r_[:2, 4:n_ch]])
- pred = output[..., :4].clone()
- pred[..., 0] += self.grid_x[output_id]
- pred[..., 1] += self.grid_y[output_id]
- pred[..., 2] = torch.exp(pred[..., 2]) * self.anchor_w[output_id]
- pred[..., 3] = torch.exp(pred[..., 3]) * self.anchor_h[output_id]
- obj_mask, tgt_mask, tgt_scale, target = self.build_target(pred, labels, batchsize, fsize, n_ch, output_id)
- # loss calculation
- output[..., 4] *= obj_mask
- output[..., np.r_[0:4, 5:n_ch]] *= tgt_mask
- output[..., 2:4] *= tgt_scale
- target[..., 4] *= obj_mask
- target[..., np.r_[0:4, 5:n_ch]] *= tgt_mask
- target[..., 2:4] *= tgt_scale
- loss_xy += F.binary_cross_entropy(input=output[..., :2], target=target[..., :2],
- weight=tgt_scale * tgt_scale, reduction='sum')
- loss_wh += F.mse_loss(input=output[..., 2:4], target=target[..., 2:4], reduction='sum') / 2
- loss_obj += F.binary_cross_entropy(input=output[..., 4], target=target[..., 4], reduction='sum')
- loss_cls += F.binary_cross_entropy(input=output[..., 5:], target=target[..., 5:], reduction='sum')
- loss_l2 += F.mse_loss(input=output, target=target, reduction='sum')
- loss = loss_xy + loss_wh + loss_obj + loss_cls
- return loss, loss_xy, loss_wh, loss_obj, loss_cls, loss_l2
- def collate(batch):
- images = []
- bboxes = []
- for img, box in batch:
- images.append([img])
- bboxes.append([box])
- images = np.concatenate(images, axis=0)
- images = images.transpose(0, 3, 1, 2)
- images = torch.from_numpy(images).div(255.0)
- bboxes = np.concatenate(bboxes, axis=0)
- bboxes = torch.from_numpy(bboxes)
- return images, bboxes
- def train(model, device, config, epochs=5, batch_size=1, save_cp=True, log_step=20, img_scale=0.5):
- train_dataset = Yolo_dataset(config.train_label, config, train=True)
- val_dataset = Yolo_dataset(config.val_label, config, train=False)
- n_train = len(train_dataset)
- n_val = len(val_dataset)
- train_loader = DataLoader(train_dataset, batch_size=config.batch // config.subdivisions, shuffle=True,
- num_workers=0, pin_memory=True, drop_last=True, collate_fn=collate)
- val_loader = DataLoader(val_dataset, batch_size=config.batch // config.subdivisions, shuffle=True, num_workers=0,
- pin_memory=True, drop_last=True, collate_fn=val_collate)
- writer = SummaryWriter(log_dir=config.TRAIN_TENSORBOARD_DIR,
- filename_suffix=f'OPT_{config.TRAIN_OPTIMIZER}_LR_{config.learning_rate}_BS_{config.batch}_Sub_{config.subdivisions}_Size_{config.width}',
- comment=f'OPT_{config.TRAIN_OPTIMIZER}_LR_{config.learning_rate}_BS_{config.batch}_Sub_{config.subdivisions}_Size_{config.width}')
- # writer.add_images('legend',
- # torch.from_numpy(train_dataset.label2colorlegend2(cfg.DATA_CLASSES).transpose([2, 0, 1])).to(
- # device).unsqueeze(0))
- max_itr = config.TRAIN_EPOCHS * n_train
- # global_step = cfg.TRAIN_MINEPOCH * n_train
- global_step = 0
- logging.info(f'''Starting training:
- Epochs: {epochs}
- Batch size: {config.batch}
- Subdivisions: {config.subdivisions}
- Learning rate: {config.learning_rate}
- Training size: {n_train}
- Validation size: {n_val}
- Checkpoints: {save_cp}
- Device: {device.type}
- Images size: {config.width}
- Optimizer: {config.TRAIN_OPTIMIZER}
- Dataset classes: {config.classes}
- Train label path:{config.train_label}
- Pretrained:
- ''')
- # learning rate setup
- def burnin_schedule(i):
- if i < config.burn_in:
- factor = pow(i / config.burn_in, 4)
- elif i < config.steps[0]:
- factor = 1.0
- elif i < config.steps[1]:
- factor = 0.1
- else:
- factor = 0.01
- return factor
- if config.TRAIN_OPTIMIZER.lower() == 'adam':
- optimizer = optim.Adam(
- model.parameters(),
- lr=config.learning_rate / config.batch,
- betas=(0.9, 0.999),
- eps=1e-08,
- )
- elif config.TRAIN_OPTIMIZER.lower() == 'sgd':
- optimizer = optim.SGD(
- params=model.parameters(),
- lr=config.learning_rate / config.batch,
- momentum=config.momentum,
- weight_decay=config.decay,
- )
- scheduler = optim.lr_scheduler.LambdaLR(optimizer, burnin_schedule)
- criterion = Yolo_loss(device=device, batch=config.batch // config.subdivisions, n_classes=config.classes)
- # scheduler = ReduceLROnPlateau(optimizer, mode='max', verbose=True, patience=6, min_lr=1e-7)
- # scheduler = CosineAnnealingWarmRestarts(optimizer, 0.001, 1e-6, 20)
- save_prefix = 'Yolov4_epoch'
- saved_models = deque()
- model.train()
- for epoch in range(epochs):
- # model.train()
- epoch_loss = 0
- epoch_step = 0
- with tqdm(total=n_train, desc=f'Epoch {epoch + 1}/{epochs}', unit='img', ncols=50) as pbar:
- for i, batch in enumerate(train_loader):
- global_step += 1
- epoch_step += 1
- images = batch[0]
- bboxes = batch[1]
- images = images.to(device=device, dtype=torch.float32)
- bboxes = bboxes.to(device=device)
- bboxes_pred = model(images)
- loss, loss_xy, loss_wh, loss_obj, loss_cls, loss_l2 = criterion(bboxes_pred, bboxes)
- # loss = loss / config.subdivisions
- loss.backward()
- epoch_loss += loss.item()
- if global_step % config.subdivisions == 0:
- optimizer.step()
- scheduler.step()
- model.zero_grad()
- if global_step % (log_step * config.subdivisions) == 0:
- writer.add_scalar('train/Loss', loss.item(), global_step)
- writer.add_scalar('train/loss_xy', loss_xy.item(), global_step)
- writer.add_scalar('train/loss_wh', loss_wh.item(), global_step)
- writer.add_scalar('train/loss_obj', loss_obj.item(), global_step)
- writer.add_scalar('train/loss_cls', loss_cls.item(), global_step)
- writer.add_scalar('train/loss_l2', loss_l2.item(), global_step)
- writer.add_scalar('lr', scheduler.get_lr()[0] * config.batch, global_step)
- pbar.set_postfix(**{'loss (batch)': loss.item(), 'loss_xy': loss_xy.item(),
- 'loss_wh': loss_wh.item(),
- 'loss_obj': loss_obj.item(),
- 'loss_cls': loss_cls.item(),
- 'loss_l2': loss_l2.item(),
- 'lr': scheduler.get_lr()[0] * config.batch
- })
- logging.debug('Train step_{}: loss : {},loss xy : {},loss wh : {},'
- 'loss obj : {},loss cls : {},loss l2 : {},lr : {}'
- .format(global_step, loss.item(), loss_xy.item(),
- loss_wh.item(), loss_obj.item(),
- loss_cls.item(), loss_l2.item(),
- scheduler.get_lr()[0] * config.batch))
- pbar.update(images.shape[0])
- if cfg.use_darknet_cfg:
- eval_model = Darknet(cfg.cfgfile, inference=True)
- else:
- eval_model = Yolov4(cfg.pretrained, n_classes=cfg.classes, inference=True)
- # eval_model = Yolov4(yolov4conv137weight=None, n_classes=config.classes, inference=True)
- if torch.cuda.device_count() > 1:
- eval_model.load_state_dict(model.module.state_dict())
- else:
- eval_model.load_state_dict(model.state_dict())
- eval_model.to(device)
- evaluator = evaluate(eval_model, val_loader, config, device)
- del eval_model
- stats = evaluator.coco_eval['bbox'].stats
- writer.add_scalar('train/AP', stats[0], global_step)
- writer.add_scalar('train/AP50', stats[1], global_step)
- writer.add_scalar('train/AP75', stats[2], global_step)
- writer.add_scalar('train/AP_small', stats[3], global_step)
- writer.add_scalar('train/AP_medium', stats[4], global_step)
- writer.add_scalar('train/AP_large', stats[5], global_step)
- writer.add_scalar('train/AR1', stats[6], global_step)
- writer.add_scalar('train/AR10', stats[7], global_step)
- writer.add_scalar('train/AR100', stats[8], global_step)
- writer.add_scalar('train/AR_small', stats[9], global_step)
- writer.add_scalar('train/AR_medium', stats[10], global_step)
- writer.add_scalar('train/AR_large', stats[11], global_step)
- if save_cp:
- try:
- # os.mkdir(config.checkpoints)
- os.makedirs(config.checkpoints, exist_ok=True)
- logging.info('Created checkpoint directory')
- except OSError:
- pass
- save_path = os.path.join(config.checkpoints, f'{save_prefix}{epoch + 1}.pth')
- if isinstance(model, torch.nn.DataParallel):
- torch.save(model.moduel,state_dict(), save_path)
- else:
- torch.save(model.state_dict(), save_path)
- logging.info(f'Checkpoint {epoch + 1} saved !')
- saved_models.append(save_path)
- if len(saved_models) > config.keep_checkpoint_max > 0:
- model_to_remove = saved_models.popleft()
- try:
- os.remove(model_to_remove)
- except:
- logging.info(f'failed to remove {model_to_remove}')
- writer.close()
- @torch.no_grad()
- def evaluate(model, data_loader, cfg, device, logger=None, **kwargs):
- """ finished, tested
- """
- # cpu_device = torch.device("cpu")
- model.eval()
- # header = 'Test:'
- coco = convert_to_coco_api(data_loader.dataset, bbox_fmt='coco')
- coco_evaluator = CocoEvaluator(coco, iou_types = ["bbox"], bbox_fmt='coco')
- for images, targets in data_loader:
- model_input = [[cv2.resize(img, (cfg.w, cfg.h))] for img in images]
- model_input = np.concatenate(model_input, axis=0)
- model_input = model_input.transpose(0, 3, 1, 2)
- model_input = torch.from_numpy(model_input).div(255.0)
- model_input = model_input.to(device)
- targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
- if torch.cuda.is_available():
- torch.cuda.synchronize()
- model_time = time.time()
- outputs = model(model_input)
- # outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
- model_time = time.time() - model_time
- # outputs = outputs.cpu().detach().numpy()
- res = {}
- # for img, target, output in zip(images, targets, outputs):
- for img, target, boxes, confs in zip(images, targets, outputs[0], outputs[1]):
- img_height, img_width = img.shape[:2]
- # boxes = output[...,:4].copy() # output boxes in yolo format
- boxes = boxes.squeeze(2).cpu().detach().numpy()
- boxes[...,2:] = boxes[...,2:] - boxes[...,:2] # Transform [x1, y1, x2, y2] to [x1, y1, w, h]
- boxes[...,0] = boxes[...,0]*img_width
- boxes[...,1] = boxes[...,1]*img_height
- boxes[...,2] = boxes[...,2]*img_width
- boxes[...,3] = boxes[...,3]*img_height
- boxes = torch.as_tensor(boxes, dtype=torch.float32)
- # confs = output[...,4:].copy()
- confs = confs.cpu().detach().numpy()
- labels = np.argmax(confs, axis=1).flatten()
- labels = torch.as_tensor(labels, dtype=torch.int64)
- scores = np.max(confs, axis=1).flatten()
- scores = torch.as_tensor(scores, dtype=torch.float32)
- res[target["image_id"].item()] = {
- "boxes": boxes,
- "scores": scores,
- "labels": labels,
- }
- evaluator_time = time.time()
- coco_evaluator.update(res)
- evaluator_time = time.time() - evaluator_time
- # gather the stats from all processes
- coco_evaluator.synchronize_between_processes()
- # accumulate predictions from all images
- coco_evaluator.accumulate()
- coco_evaluator.summarize()
- return coco_evaluator
- def get_args(**kwargs):
- cfg = kwargs
- parser = argparse.ArgumentParser(description='Train the Model on images and target masks',
- formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- # parser.add_argument('-b', '--batch-size', metavar='B', type=int, nargs='?', default=2,
- # help='Batch size', dest='batchsize')
- parser.add_argument('-l', '--learning-rate', metavar='LR', type=float, nargs='?', default=0.001,
- help='Learning rate', dest='learning_rate')
- parser.add_argument('-f', '--load', dest='load', type=str, default=None,
- help='Load model from a .pth file')
- parser.add_argument('-g', '--gpu', metavar='G', type=str, default='-1',
- help='GPU', dest='gpu')
- parser.add_argument('-dir', '--data-dir', type=str, default=None,
- help='dataset dir', dest='dataset_dir')
- parser.add_argument('-pretrained', type=str, default=None, help='pretrained yolov4.conv.137')
- parser.add_argument('-classes', type=int, default=80, help='dataset classes')
- parser.add_argument('-train_label_path', dest='train_label', type=str, default='train.txt', help="train label path")
- parser.add_argument(
- '-optimizer', type=str, default='adam',
- help='training optimizer',
- dest='TRAIN_OPTIMIZER')
- parser.add_argument(
- '-iou-type', type=str, default='iou',
- help='iou type (iou, giou, diou, ciou)',
- dest='iou_type')
- parser.add_argument(
- '-keep-checkpoint-max', type=int, default=10,
- help='maximum number of checkpoints to keep. If set 0, all checkpoints will be kept',
- dest='keep_checkpoint_max')
- args = vars(parser.parse_args())
- # for k in args.keys():
- # cfg[k] = args.get(k)
- cfg.update(args)
- return edict(cfg)
- def init_logger(log_file=None, log_dir=None, log_level=logging.INFO, mode='w', stdout=True):
- """
- log_dir: 日志文件的文件夹路径
- mode: 'a', append; 'w', 覆盖原文件写入.
- """
- def get_date_str():
- now = datetime.datetime.now()
- return now.strftime('%Y-%m-%d_%H-%M-%S')
- fmt = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s: %(message)s'
- if log_dir is None:
- log_dir = '~/temp/log/'
- if log_file is None:
- log_file = 'log_' + get_date_str() + '.txt'
- if not os.path.exists(log_dir):
- os.makedirs(log_dir)
- log_file = os.path.join(log_dir, log_file)
- # 此处不能使用logging输出
- print('log file path:' + log_file)
- logging.basicConfig(level=logging.DEBUG,
- format=fmt,
- filename=log_file,
- filemode=mode)
- if stdout:
- console = logging.StreamHandler(stream=sys.stdout)
- console.setLevel(log_level)
- formatter = logging.Formatter(fmt)
- console.setFormatter(formatter)
- logging.getLogger('').addHandler(console)
- return logging
- def _get_date_str():
- now = datetime.datetime.now()
- return now.strftime('%Y-%m-%d_%H-%M')
- if __name__ == "__main__":
- logging = init_logger(log_dir='log')
- cfg = get_args(**Cfg)
- os.environ["CUDA_VISIBLE_DEVICES"] = cfg.gpu
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- logging.info(f'Using device {device}')
- if cfg.use_darknet_cfg:
- model = Darknet(cfg.cfgfile)
- else:
- model = Yolov4(cfg.pretrained, n_classes=cfg.classes)
- if torch.cuda.device_count() > 1:
- model = torch.nn.DataParallel(model)
- model.to(device=device)
- try:
- train(model=model,
- config=cfg,
- epochs=cfg.TRAIN_EPOCHS,
- device=device, )
- except KeyboardInterrupt:
- if isinstance(model, torch.nn.DataParallel):
- torch.save(model.module.state_dict(), 'INTERRUPTED.pth')
- else:
- torch.save(model.state_dict(), 'INTERRUPTED.pth')
- logging.info('Saved interrupt')
- try:
- sys.exit(0)
- except SystemExit:
- os._exit(0)
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